Low-complexity, highly-spectrally-efficient communications

ABSTRACT

A system may comprise circuitry that includes a sequence estimation circuit and a non-linearity modeling circuit. The circuitry may be operable to receive a single-carrier signal that was generated by passage of symbols through a partial response filter and through a non-linear circuit. The circuitry may be operable to generate estimated values of the symbols using the sequence estimation circuit and using the non-linearity modeling circuit. An output of the non-linearity modeling circuit may be equal to a corresponding input of the non-linearity modeling circuit modified according to a non-linear model that approximates the non-linearity of the non-linear circuit through which the received signal passed.

CLAIM OF PRIORITY

This patent application is a continuation of U.S. patent application Ser. No. 13/754,964 filed on Jan. 31, 2013 (now patented as U.S. Pat. No. 8,582,637), which in turn, claims priority to U.S. Provisional Patent Application Ser. No. 61/662,085 titled “Apparatus and Method for Efficient Utilization of Bandwidth” and filed on Jun. 20, 2012, now expired. This patent application is also a non-provisional of U.S. Provisional Patent Application Ser. No. 61/726,099 titled “Modulation Scheme Based on Partial Response” and filed on Nov. 14, 2012, U.S. Provisional Patent Application Ser. No. 61/729,774 titled “Modulation Scheme Based on Partial Response” and filed on Nov. 26, 2012; and U.S. Provisional Patent Application Ser. No. 61/747,132 titled “Modulation Scheme Based on Partial Response” and filed on Dec. 28, 2012.

Each of the above-identified applications is hereby incorporated herein by reference in its entirety.

INCORPORATIONS BY REFERENCE

This patent application makes reference to:

-   U.S. patent application Ser. No. 13/754,998 titled “Design and     Optimization of Partial Response Pulse Shape Filter,” and filed on     Jan. 31, 2013; -   U.S. patent application Ser. No. 13/755,001 titled “Constellation     Map Optimization for Highly Spectrally Efficient Communications,”     and filed on Jan. 31, 2013; -   U.S. Pat. No. 8,571,131 titled “Dynamic Filter Adjustment for     Highly-Spectrally-Efficient Communications”; -   U.S. Pat. No. 8,559,494 titled “Timing Synchronization for Reception     of Highly-Spectrally-Efficient Communications;” -   U.S. patent application Ser. No. 13/755,018 titled “Feed Forward     Equalization for Highly-Spectrally-Efficient Communications,” and     filed on Jan. 31, 2013; -   U.S. patent application Ser. No. 13/755,021 titled “Decision     Feedback Equalizer for Highly-Spectrally-Efficient Communications,”     and filed on Jan. 31, 2013; -   U.S. patent application Ser. No. 13/755,025 titled “Decision     Feedback Equalizer with Multiple Cores for     Highly-Spectrally-Efficient Communications,” and filed on Jan. 31,     2013; -   U.S. Pat. No. 8,559,498 titled “Decision Feedback Equalizer     Utilizing Symbol Error Rate Biased Adaptation Function for     Highly-Spectrally-Efficient Communications;” -   U.S. Pat. No. 8,548,097 titled “Coarse Phase Estimation for     Highly-Spectrally-Efficient Communications”; -   U.S. Pat. No. 8,565,363 titled “Fine Phase Estimation for Highly     Spectrally Efficient Communications;” and -   U.S. patent application Ser. No. 13/755,043, titled “Joint Sequence     Estimation of Symbol and Phase with High Tolerance of Nonlinearity,”     and filed on Jan. 31, 2013.

Each of the above identified applications is hereby incorporated herein by reference in its entirety.

TECHNICAL FIELD

Aspects of the present application relate to electronic communications.

BACKGROUND

Existing communications methods and systems are overly power hungry and/or spectrally inefficient. Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of such approaches with some aspects of the present method and system set forth in the remainder of this disclosure with reference to the drawings.

BRIEF SUMMARY

Methods and systems are provided for low-complexity, highly-spectrally efficient communications, substantially as illustrated by and/or described in connection with at least one of the figures, as set forth more completely in the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram depicting an example system configured for low-complexity, highly-spectrally-efficient communications.

FIG. 2 is a block diagram depicting an example equalization and sequence estimation circuit for use in a system configured for low-complexity, highly-spectrally-efficient communications.

FIG. 3 is a block diagram depicting an example sequence estimation circuit for use in a system configured for low-complexity, highly-spectrally-efficient communications.

FIG. 4 is a block diagram depicting an example metric calculation circuit for use in a system configured for low-complexity, highly-spectrally-efficient communications.

FIGS. 5A-5D depict portions of an example sequence estimation process performed by a system configured for low-complexity, highly-spectrally-efficient communications.

FIGS. 6A and 6B depict an example survivor selection process that is an alternative to the process depicted in FIG. 5D.

FIG. 6C is a flowchart illustrating dynamic control of sequence estimation parameters based on characteristics of a received partial response signal.

FIG. 7 is a diagram illustrating initialization of the sequence estimation process.

FIGS. 8A depicts an example implementation of the phase buffer shown in FIG. 3.

FIGS. 8B depicts an example implementation of the symbol buffer shown in FIG. 3.

FIG. 8C depicts contents of an example symbol buffer over a plurality of iterations of a sequence estimation process.

FIG. 8D depicts generated signals corresponding to the symbol buffer contents shown in FIG. 8C.

FIG. 9 is a flowchart illustrating an example process for communications using partial response signaling.

FIG. 10 is a flowchart illustrating an example process for equalization, phase error correction, and sequence estimation for reception of partial response signals.

FIG. 11 is a flowchart illustrating an example process for sequence estimation for reception of partial response signals.

DETAILED DESCRIPTION

As utilized herein the terms “circuits” and “circuitry” refer to physical electronic components (i.e. hardware) and any software and/or firmware (“code”) which may configure the hardware, be executed by the hardware, and or otherwise be associated with the hardware. As used herein, for example, a particular processor and memory may comprise a first “circuit” when executing a first one or more lines of code and may comprise a second “circuit” when executing a second one or more lines of code. As utilized herein, “and/or” means any one or more of the items in the list joined by “and/or”. As an example, “x and/or y” means any element of the three-element set {(x), (y), (x, y)}. As another example, “x, y, and/or z” means any element of the seven-element set {(x), (y), (z), (x, y), (x, z), (y, z), (x, y, z)}. As utilized herein, the term “exemplary” means serving as a non-limiting example, instance, or illustration. As utilized herein, the terms “e.g.,” and “for example” set off lists of one or more non-limiting examples, instances, or illustrations. As utilized herein, circuitry is “operable” to perform a function whenever the circuitry comprises the necessary hardware and code (if any is necessary) to perform the function, regardless of whether performance of the function is disabled, or not enabled, by some user-configurable setting.

FIG. 1 is a block diagram depicting an example system configured for low-complexity, highly-spectrally-efficient communications. The system 100 comprises a mapper circuit 102, a pulse shaping filter circuit 104, a timing pilot insertion circuit 105, a transmitter front-end circuit 106, a channel 107, a receiver front-end 108, a filter circuit 109, a timing pilot removal circuit 110, an equalization and sequence estimation circuit 112, and a de-mapping circuit 114. The components 102, 104, 105, and 106 may be part of a transmitter (e.g., a base station or access point, a router, a gateway, a mobile device, a server, a computer, a computer peripheral device, a table, a modem, a set-top box, etc.), the components 108, 109, 110, 112, and 114 may be part of a receiver (e.g., a base station or access point, a router, a gateway, a mobile device, a server, a computer, a computer peripheral device, a table, a modem, a set-top box, etc.), and the transmitter and receiver may communicate via the channel 107.

The mapper 102 may be operable to map bits of the Tx_bitstream to be transmitted to symbols according to a selected modulation scheme. The symbols may be output via signal 103. For example, for an quadrature amplitude modulation scheme having a symbol alphabet of N (N-QAM), the mapper may map each Log₂(N) bits of the Tx_bitstream to single symbol represented as a complex number and/or as in-phase (I) and quadrature-phase (Q) components. Although N-QAM is used for illustration in this disclosure, aspects of this disclosure are applicable to any modulation scheme (e.g., amplitude shift keying (ASK), phase shift keying (PSK), frequency shift keying (FSK), etc.). Additionally, points of the N-QAM constellation may be regularly spaced (“on-grid”) or irregularly spaced (“off-grid”). Furthermore, the symbol constellation used by the mapper may be optimized for best bit-error rate performance that is related to log-likelihood ratio (LLR) and to optimizing mean mutual information bit (MMIB). The Tx_bitstream may, for example, be the result of bits of data passing through a forward error correction (FEC) encoder and/or an interleaver. Additionally, or alternatively, the symbols out of the mapper 102 may pass through an interleaver.

The pulse shaper 104 may be operable to adjust the waveform of the signal 103 such that the waveform of the resulting signal 113 complies with the spectral requirements of the channel over which the signal 113 is to be transmitted. The spectral requirements may be referred to as the “spectral mask” and may be established by a regulatory body (e.g., the Federal Communications Commission in the United States or the European Telecommunications Standards Institute) and/or a standards body (e.g., Third Generation Partnership Project) that governs the communication channel(s) and/or standard(s) in use. The pulse shaper 104 may comprise, for example, an infinite impulse response (IIR) and/or a finite impulse response (FIR) filter. The number of taps, or “length,” of the pulse shaper 104 is denoted herein as LTx, which is an integer. The impulse response of the pulse shaper 104 is denoted herein as hTx. The pulse shaper 104 may be configured such that its output signal 113 intentionally has a substantial amount of inter-symbol interference (ISI). Accordingly, the pulse shaper 104 may be referred to as a partial response pulse shaping filter, and the signal 113 may be referred to as a partial response signal or as residing in the partial response domain, whereas the signal 103 may be referred to as residing in the symbol domain. The number of taps and/or the values of the tap coefficients of the pulse shaper 104 may be designed such that the pulse shaper 104 is intentionally non-optimal for additive white Gaussian noise (AWGN) in order to improve tolerance of non-linearity in the signal path. In this regard, the pulse shaper 104 may offer superior performance in the presence of non-linearity as compared to, for example, a conventional near zero positive ISI pulse shaping filter (e.g., root raised cosine (RRC) pulse shaping filter). The pulse shaper 104 may be designed as described in one or more of: the United States patent application titled “Design and Optimization of Partial Response Pulse Shape Filter,” the United States patent application titled “Constellation Map Optimization For Highly Spectrally Efficient Communications,” and the United States patent application titled “Dynamic Filter Adjustment For Highly-Spectrally-Efficient Communications,” each of which is incorporated herein by reference, as set forth above.

It should be noted that a partial response signal (or signals in the “partial response domain”) is just one example of a type of signal for which there is correlation among symbols of the signal (referred to herein as “inter-symbol-correlated (ISC) signals”). Such ISC signals are in contrast to zero (or near-zero) ISI signals generated by, for example, raised-cosine (RC) or root-raised-cosine (RRC) filtering. For simplicity of illustration, this disclosure focuses on partial response signals generated via partial response filtering. Nevertheless, aspects of this disclosure are applicable to other ISC signals such as, for example, signals generated via matrix multiplication (e.g., lattice coding), and signals generated via decimation below the Nyquist frequency such that aliasing creates correlation between symbols.

The timing pilot insertion circuit 105 may insert a pilot signal which may be utilized by the receiver for timing synchronization. The output signal 115 of the timing pilot insertion circuit 105 may thus comprise the signal 113 plus an inserted pilot signal (e.g., a sine wave at ¼fbaud, where fbaud is the symbol rate). An example implementation of the pilot insertion circuit 105 is described in the United States patent application titled “Timing Synchronization for Reception of Highly-Spectrally-Efficient Communications,” which is incorporated herein by reference, as set forth above.

The transmitter front-end 106 may be operable to amplify and/or upconvert the signal 115 to generate the signal 116. Thus, the transmitter front-end 106 may comprise, for example, a power amplifier and/or a mixer. The front-end may introduce non-linear distortion and/or phase noise (and/or other non-idealities) to the signal 116. The non-linearity of the circuit 106 may be represented as Fn1Tx which may be, for example, a polynomial, or an exponential (e.g., Rapp model). The non-linearity may incorporate memory (e.g., Voltera series).

The channel 107 may comprise a wired, wireless, and/or optical communication medium. The signal 116 may propagate through the channel 107 and arrive at the receive front-end 108 as signal 118. Signal 118 may be noisier than signal 116 (e.g., as a result of thermal noise in the channel) and may have higher or different ISI than signal 116 (e.g., as a result of multi-path).

The receiver front-end 108 may be operable to amplify and/or downconvert the signal 118 to generate the signal 119. Thus, the receiver front-end may comprise, for example, a low-noise amplifier and/or a mixer. The receiver front-end may introduce non-linear distortion and/or phase noise to the signal 119. The non-linearity of the circuit 108 may be represented as Fn1Rx which may be, for example, a polynomial, or an exponential (e.g., Rapp model). The non-linearity may incorporate memory (e.g., Voltera series).

The timing pilot recovery and removal circuit 110 may be operable to lock to the timing pilot signal inserted by the pilot insertion circuit 105 in order to recover the symbol timing of the received signal. The output 122 may thus comprise the signal 120 minus (i.e., without) the timing pilot signal. An example implementation of the timing pilot recovery and removal circuit 110 is described in the United States patent application titled “Timing Synchronization for Reception of Highly-Spectrally-Efficient Communications,” which is incorporated herein by reference, as set forth above.

The input filter 109 may be operable to adjust the waveform of the partial response signal 119 to generate partial response signal 120. The input filter 109 may comprise, for example, an infinite impulse response (IIR) and/or a finite impulse response (FIR) filter. The number of taps, or “length,” of the input filter 109 is denoted herein as LRx, an integer. The impulse response of the input filter 109 is denoted herein as hRx. The number of taps, and/or tap coefficients of the pulse shaper 109 may be configured based on: a non-linearity model, F{circumflex over (n)}l, signal-to-noise ratio (SNR) of signal 120, the number of taps and/or tap coefficients of the Tx partial response filter 104, and/or other parameters. The number of taps and/or the values of the tap coefficients of the input filter 109 may be configured such that noise rejection is intentionally compromised (relative to a perfect match filter) in order to improve performance in the presence of non-linearity. As a result, the input filter 109 may offer superior performance in the presence of non-linearity as compared to, for example, a conventional near zero positive ISI matching filter (e.g., root raised cosine (RRC) matched filter). The input filter 109 may be designed as described in one or more of: the United States patent application titled “Design and Optimization of Partial Response Pulse Shape Filter,” the United States patent application titled “Constellation Map Optimization For Highly Spectrally Efficient Communications,” and the United States patent application titled “Dynamic Filter Adjustment For Highly-Spectrally-Efficient Communications,” each of which is incorporated herein by reference, as set forth above.

As utilized herein, the “total partial response (h)” may be equal to the convolution of hTx and hRx, and, thus, the “total partial response length (L)” may be equal to LTx+LRx−1. L may, however, be chosen to be less than LTx+LRx−1 where, for example, one or more taps of the Tx pulse shaper 104 and/or the Rx input filter 109 are below a determined level. Reducing L may reduce decoding complexity of the sequence estimation. This tradeoff may be optimized during the design of the system 100.

The equalizer and sequence estimator 112 may be operable to perform an equalization process and a sequence estimation process. Details of an example implementation of the equalizer and sequence estimator 112 are described below with respect to FIG. 2. The output signal 132 of the equalizer and sequence estimator 112 may be in the symbol domain and may carry estimated values of corresponding transmitted symbols (and/or estimated values of the corresponding transmitted information bits of the Tx_bitstream) of signal 103. Although not depicted, the signal 132 may pass through an interleaver en route to the de-mapper 114. The estimated values may comprise soft-decision estimates, hard-decision estimates, or both.

The de-mapper 114 may be operable to map symbols to bit sequences according to a selected modulation scheme. For example, for an N-QAM modulation scheme, the mapper may map each symbol to Log₂(N) bits of the Rx_bitstream. The Rx_bitstream may, for example, be output to a de-interleaver and/or an FEC decoder. Alternatively, or additionally, the de-mapper 114 may generate a soft output for each bit, referred as LLR (Log-Likelihood Ratio). The soft output bits may be used by a soft-decoding forward error corrector (e.g. a low-density parity check (LDPC) decoder). The soft output bits may be generated using, for example, a Soft Output Viterbi Algorithm (S OVA) or similar. Such algorithms may use additional information of the sequence decoding process including metrics levels of dropped paths and/or estimated bit probabilities for generating the LLR, where LLR (b)=log(P_(b)/1−P_(b)), where P_(b) is the probability that bit b=1.

In an example implementation, components of the system upstream of the pulse shaper 104 in the transmitter and downstream of the equalizer and sequence estimator 112 in the receiver may be as found in a conventional N-QAM system. Thus, through modification of the transmit side physical layer and the receive side physical layer, aspects of the invention may be implemented in an otherwise conventional N-QAM system in order to improve performance of the system in the presence of non-linearity as compared, for example, to use of RRC filters and an N-QAM slicer.

FIG. 2 is a block diagram depicting an example equalization and sequence estimation circuit for use in a system configured for low-complexity, highly-spectrally-efficient communications. Shown are an equalizer circuit 202, a signal combiner circuit 204, a phase adjust circuit 206, a sequence estimation circuit 210, and non-linearity modeling circuits 236 a and 236 b.

The equalizer 202 may be operable to process the signal 122 to reduce ISI caused by the channel 107. The output 222 of the equalizer 202 is a partial response domain signal. The ISI of the signal 222 is primarily the result of the pulse shaper 104 and the input filter 109 (there may be some residual ISI from multipath, for example, due to use of the least means square (LMS) approach in the equalizer 202). The error signal, 201, fed back to the equalizer 202 is also in the partial response domain. The signal 201 is the difference, calculated by combiner 204, between 222 and a partial response signal 203 that is output by non-linearity modeling circuit 236 a. An example implementation of the equalizer is described in the United States patent application titled “Feed Forward Equalization for Highly-Spectrally-Efficient Communications,” which is incorporated herein by reference, as set forth above.

The carrier recovery circuit 208 may be operable to generate a signal 228 based on a phase difference between the signal 222 and a partial response signal 207 output by the non-linearity modeling circuit 236 b. The carrier recovery circuit 208 may be as described in the United States patent application titled “Coarse Phase Estimation for Highly-Spectrally-Efficient Communications,” which is incorporated herein by reference, as set forth above.

The phase adjust circuit 206 may be operable to adjust the phase of the signal 222 to generate the signal 226. The amount and direction of the phase adjustment may be determined by the signal 228 output by the carrier recovery circuit 208. The signal 226 is a partial response signal that approximates (up to an equalization error caused by finite length of the equalizer 202, a residual phase error not corrected by the phase adjust circuit 206, non-linearities, and/or other non-idealities) the total partial response signal resulting from corresponding symbols of signal 103 passing through pulse shaper 104 and input filter 109.

The buffer 212 buffers samples of the signal 226 and outputs a plurality of samples of the signal 226 via signal 232. The signal 232 is denoted PR1, where the underlining indicates that it is a vector (in this case each element of the vector corresponds to a sample of a partial response signal). In an example implementation, the length of the vector PR1 may be Q samples.

Input to the sequence estimation circuit 210 are the signal 232, the signal 228, and a response ĥ. Response ĥ is based on h (the total partial response, discussed above). For example, response ĥ may represent a compromise between h (described above) and a filter response that compensates for channel non-idealities such as multi-path. The response ĥ may be conveyed and/or stored in the form of LTx+LRx−1 tap coefficients resulting from convolution of the LTx tap coefficients of the pulse shaper 104 and the LRx tap coefficients of the input filter 109. Alternatively, response ĥ may be conveyed and/or stored in the form of fewer than LTx+LRx−1 tap coefficients—for example, where one or more taps of the LTx and LRx is ignored due to being below a determined threshold. The sequence estimation circuit 210 may output partial response feedback signals 205 and 209, a signal 234 that corresponds to the finely determined phase error of the signal 120, and signal 132 (which carries hard and/or soft estimates of transmitted symbols and/or transmitted bits). An example implementation of the sequence estimation circuit 210 is described below with reference to FIG. 3.

The non-linear modeling circuit 236 a may apply a non-linearity function {circumflex over (F)}{circumflex over (n)}{circumflex over (l)} (a model of the non-linearity seen by the received signal en route to the circuit 210) to the signal 205 resulting in the signal 203. Similarly, the non-linear modeling circuit 236 b may apply the non-linearity function {circumflex over (F)}{circumflex over (n)}{circumflex over (l)} to the signal 209 resulting in the signal 207. {circumflex over (F)}{circumflex over (n)}{circumflex over (l)} may be, for example, a third-order or fifth-order polynomial. Increased accuracy resulting from the use of a higher-order polynomial for {circumflex over (F)}{circumflex over (n)}{circumflex over (l)} may tradeoff with increased complexity of implementing a higher-order polynomial. Where FnlTx is the dominant non-linearity of the communication system 100, {circumflex over (F)}{circumflex over (n)}{circumflex over (l)} modeling only FnlTx may be sufficient. Where degradation in receiver performance is above a threshold due to other non-linearities in the system (e.g., non-linearity of the receiver front-end 108) the model {circumflex over (F)}{circumflex over (n)}{circumflex over (l)} may take into account such other non-linearities

FIG. 3 is a block diagram depicting an example sequence estimation circuit for use in a system configured for low-complexity, highly-spectrally-efficient communications. Shown are a candidate generation circuit 302, a metrics calculation circuit 304, a candidate selection circuit 306, a combiner circuit 308, a buffer circuit 310, a buffer circuit 312, a phase adjust circuit 314, and convolution circuits 316 a and 316 b. The sequence estimation process described with respect to FIG. 3 is an example only. Many variations of the sequence estimation process are also possible. For example, although the implementation described here uses one phase survivor per symbol survivor, another implementation may have PSu (e.g., PSu<Su) phase survivors that will be used commonly for each symbol survivor.

For each symbol candidate at time n, the metrics calculation circuit 304 may be operable to generate a metric vector D_(n) ¹ . . . D_(n) ^(M×Su×P) based on the partial response signal PR1, the signal 303 a conveying the phase candidate vectors PC_(n) ¹ . . . PC_(n) ^(M×Su×P), and the signal 303 b conveying the symbol candidate vectors SC_(n) ¹ . . . SC_(n) ^(M×Su×P), where underlining indicates a vector, subscript n indicates that it is the candidate vectors for time n, M is an integer equal to the size of the symbol alphabet (e.g., for N-QAM, M is equal to N), Su is an integer equal to the number of symbol survivor vectors retained for each iteration of the sequence estimation process, and P is an integer equal to the size of the phase alphabet. In an example implementation, the size of phase alphabet is three, with each of the three symbols corresponding to one of: a positive shift, a negative phase shift, or zero phase shift, as further described below with respect to FIGS. 5A-5D and in the United States patent application titled “Fine Phase Estimation for Highly Spectrally Efficient Communications,” which is incorporated herein by reference, as set forth above. In an example implementation, each phase candidate vector may comprise Q phase values and each symbol candidate vector may comprise Q symbols. An example implementation of the metrics calculation block is described below with reference to FIG. 4.

The candidate selection circuit 306 may be operable to select Su of the symbol candidates SC_(n) ¹ . . . SC_(n) ^(M×Su×P) and Su of the phase candidates PC_(n) ¹ . . . PC_(n) ^(M×Su×P) based on the metrics D_(n) ¹ . . . D_(n) ^(M×Su×P). The selected phase candidates are referred to as the phase survivors PS_(n) ¹ . . . PS_(n) ^(Su). Each element of each phase survivors PS_(n) ¹ . . . PS_(n) ^(Su) may correspond to an estimate of residual phase error in the signal 232. That is, the phase error remaining in the signal after coarse phase error correction via the phase adjust circuit 206. The best phase survivor PS_(n) ¹ is conveyed via signal 307 a. The Su phase survivors are retained for the next iteration of the sequence estimation process (at which time they are conveyed via signal 301 b ). The selected symbol candidates are referred to as the symbol survivors SS_(n) ¹ . . . SS_(n) ^(Su). Each element of each symbol survivors SS_(n) ¹ . . . SS_(n) ^(Su) may comprise a soft-decision estimate and/or a hard-decision estimate of a symbol and/or information bits of the signal 232. The best symbol survivor SS¹ _(n) is conveyed to symbol buffer 310 via the signal 307 b. The Su symbol survivors are retained for the next iteration of the sequence estimation process (at which time they are conveyed via signal 301 a ). Although, the example implementation described selects the same number, Su, of phase survivors and symbol survivors, such is not necessarily the case. Operation of example candidate selection circuits 306 are described below with reference to FIGS. 5D and 6A-6B.

The candidate generation circuit 302 may be operable to generate phase candidates PC_(n) ¹ . . . PC_(n) ^(M×Su×P) and symbol candidates SC_(n) ¹ . . . SC_(n) ^(M×Su×P) from phase survivors PS_(n−1) ¹ . . . PS_(n−1) ^(Su) and symbol survivors SS_(n−1) ¹ . . . SS_(n−1) ^(Su), wherein the index n−1 indicates that they are survivors from time n−1 are used for generating the candidates for time n. In an example implementation, generation of the phase and/or symbol candidates may be as, for example, described below with reference to FIGS. 5A and 5B and/or in the United States patent application titled “Joint Sequence Estimation of Symbol and Phase with High Tolerance of Nonlinearity,” which is incorporated herein by reference, as set forth above.

The symbol buffer circuit 310 may comprise a plurality of memory elements operable to store one or more symbol survivor elements of one or more symbol survivor vectors. The phase buffer circuit 312 may comprise a plurality of memory elements operable to store one or more phase survivor vectors. Example implementations of the buffers 310 and 312 are described below with reference to FIGS. 8A and 8B, respectively.

The combiner circuit 308 may be operable to combine the best phase survivor, PS_(n) ¹, conveyed via signal 307 a, with the signal 228 generated by the carrier recovery circuit 208 (FIG. 2) to generate fine phase error vector FPE_(n) ¹, conveyed via signal 309, which corresponds to the finely estimated phase error of the signal 222 (FIG. 2). At each time n, fine phase error vector FPE_(n−1) ¹ stored in phase buffer 312 may be overwritten by FPE_(n) ¹.

The phase adjust circuit 314 may be operable to adjust the phase of the signal 315 a by an amount determined by the signal 234 output by phase buffer 312, to generate the signal 205.

The circuit 316 a, which performs a convolution, may comprise a FIR filter or IIR filter, for example. The circuit 316 a may be operable to convolve the signal 132 with response, resulting in the partial response signal 315 a. Similarly, the convolution circuit 316 b may be operable to convolve the signal 317 with response ĥ, resulting in the partial response signal 209. As noted above, response ĥ may be stored by, and/or conveyed to, the sequence estimation circuit 210 in the form of one or more tap coefficients, which may be determined based on the tap coefficients of the pulse shaper 104 and/or input filter 109 and/or based on an adaptation algorithm of a decision feedback equalizer (DFE). Response ĥ may thus represent a compromise between attempting to perfectly reconstruct the total partial response signal (103 as modified by pulse shaper 104 and input filter 109) on the one hand, and compensating for multipath and/or other non-idealities of the channel 107 on the other hand. In this regard, the system 100 may comprise one or more DFEs as described in one or more of: the United States patent application titled “Decision Feedback Equalizer for Highly-Spectrally-Efficient Communications,” the United States patent application titled “Decision Feedback Equalizer with Multiple Cores for Highly-Spectrally-Efficient Communications,” and the United States patent application titled “Decision Feedback Equalizer Utilizing Symbol Error Rate Biased Adaptation Function for Highly-Spectrally-Efficient Communications,” each of which is incorporated herein by reference, as set forth above.

Thus, signal 203 is generated by taking a first estimate of transmitted symbols, (an element of symbol survivor SS_(n) ¹), converting the first estimate of transmitted symbols to the partial response domain via circuit 316 a, and then compensating for non-linearity in the communication system 100 via circuit 236 a (FIG. 2). Similarly, signal 207 is generated from a second estimate of transmitted symbols (an element of symbol survivor SS_(n) ¹) that is converted to the partial response domain by circuit 316 b to generate signal 209, and then applying a non-linear model to the signal 209b to compensate for non-linearity in the signal path.

FIG. 4 is a block diagram depicting an example metric calculation circuit for use in a system configured for low-complexity, highly-spectrally-efficient communications. Shown is a phase adjust circuit 402, a convolution circuit 404, and a cost function calculation circuit 406. The phase adjust circuit 402 may phase shift one or more elements of the vector PR1 (conveyed via signal 232) by a corresponding one or more values of the phase candidate vectors PC_(n) ¹ . . . PC_(n) ^(M×Su×P). The signal 403 output by the phase adjust circuit 402 thus conveys a plurality of partial response vectors PR2 _(n) ¹ . . . PR2 _(n) ^(M×Su×P), each of which comprises a plurality of phase-adjusted versions of PR1.

The circuit 404, which performs a convolution, may comprise a FIR filter or IIR filter, for example. The circuit 404 may be operable to convolve the symbol candidate vectors SC_(n) ¹ . . . SC_(n) ^(M×Su×P) with ĥ. The signal 405 output by the circuit 404 thus conveys vectors SCPR_(n) ¹ . . . SCPR_(n) ^(M×Su×P), each of which is a candidate partial response vector.

The cost function circuit 406 may be operable to generate metrics indicating the similarity between one or more of the partial response vectors PR2 _(n) ¹ . . . PR2 _(n) ^(M×Su×P) and one or more of the vectors SCPR_(n) ¹ . . . SCPR_(n) ^(M×Su×P) to generate error metrics D_(n) ¹ . . . D_(n) ^(M×Su×P). In an example implementation, the error metrics may be Euclidean distances calculated as shown below in equation 1.

D _(n) ^(i) 32 |(SCPR _(n) ^(i))−(PR2_(n) ^(i))|²   EQ. 1

for 1≦i≦M×Su×P.

FIGS. 5A-5D depict portions of an example sequence estimation process performed by a system configured for low-complexity, highly-spectrally-efficient communications. In FIGS. 5A-5D it is assumed, for purposes of illustration, that M=4 (a symbol alphabet of α, β, χ, δ), Su=3 (three symbol survivors are selected each iteration), Psu=Su (three phase survivors are selected each iteration), P=3 (a phase alphabet of plus, minus, and zero), and that Q (vector length) is 4.

Referring to FIG. 5A, there is shown phase and symbol survivors from time n−1 on the left side of the figure. The first step in generating symbol candidates and phase candidates from the survivors is to duplicate the survivors and shift the contents to free up an element in each of the resulting vectors called out as 502 on the right side of FIG. 5A. In the example implementation depicted, the survivors are duplicated M*P−1 times and shifted one element.

Referring to FIG. 5B, the next step in generating the candidates is inserting symbols in the vacant elements of the symbol vectors and phase values in the vacant elements of the phase vectors, resulting in the symbol candidates and phase candidate for time n (called out as 504 in FIG. 5B). In the example implementation depicted, each of the M possible symbol values is inserted into Su*P symbol candidates, and each of the P phase values may be inserted into M*Su candidates. In the example implementation depicted, θ5 is a reference phase value calculated based on phase survivor PS_(n−1) ¹. For example, θ5 may be the average (or a weighted average) of the last two or more elements of the phase survivor PS_(n−1) ¹ (in the example shown, the average over the last two elements would be (θ5+0)/2). In the example implementation depicted, θ4=θ5−Δθ, and θ6=θ5+Δθ, where Δθ is based on: the amount of phase noise in signal 226, slope (derivative) of the phase noise in signal 226, signal-to-noise ratio (SNR) of signal 226, and/or capacity of the channel 107. Similarly, in the example implementation shown, θ8 is a reference phase value calculated based on phase survivor PS_(n−1) ², θ7=θ8−Δθ, θ9=θ8+Δθ, θ11 is a reference phase value calculated based on phase survivor PS_(n−1) ³, θ10=θ11−Δθ, and θ12=θ11+Δθ.

Referring to FIG. 5C, as described above with reference to FIG. 4, the symbol candidates are transformed to the partial response domain via a convolution, the reference signal PR1 is phase adjusted, and then the metrics D_(n) ¹ . . . D_(n) ^(M×Su×P) are calculated based on the partial response signals PR2 _(n) ¹ . . . PR2 _(n) ^(M×Su×P) and SCPR_(n) ¹ . . . SCPR_(n) ^(M×Su×P).

Referring to FIG. 5D, the metrics calculated in FIG. 5C are used to select which of the candidates generated in FIG. 5B are selected to be the survivors for the next iteration of the sequence estimation process. FIG. 5D depicts an example implementation in which the survivors are selected in a single step by simply selecting Su candidates corresponding to the Su best metrics. In the example implementation depicted, it is assumed that metric D_(n) ¹⁴ is the best metric, that D_(n) ¹⁶ is the second best metric, and that D_(n) ³⁰ is the third-best metric. Accordingly, symbol candidate SC_(n) ¹⁴ is selected as the best symbol survivor, PC_(n) ¹⁴ is selected as the best phase survivor, symbol candidate SC_(n) ¹⁶ is selected as the second-best symbol survivor, PC_(n) ¹⁶ is selected as the second-best phase survivor, symbol candidate SC_(n) ³⁰ is selected as the third-best symbol survivor, and PC_(n) ³⁰ is selected as the third-best phase survivor. The survivor selection process of FIG. 5D may result in selecting identical symbol candidates which may be undesirable. A survivor selection process that prevents redundant symbol survivors is described below with reference to FIGS. 6A and 6B.

FIGS. 6A and 6B depict an example survivor selection process that is an alternative to the process depicted in FIG. 5D. In FIG. 6A, the candidates generated in FIG. 5B and the metrics calculated in FIG. 5C are used to select the best phase candidate for each symbol candidate (selected candidates are called out by reference designator 602). In FIG. 6B, the best Su of the candidates selected in FIG. 6A are selected as the survivors for the next iteration of the sequence estimation process. In the example implementation depicted, it is assumed that metric D_(n) ⁶ is the best metric, that D_(n) ⁵ is the second-best metric, and that D_(n) ²⁵ is the third-best metric. Accordingly, symbol candidate SC_(n) ⁶ is selected as the best symbol survivor, PC_(n) ⁶ is selected as the best phase survivor, symbol candidate SC_(n) ⁵ is selected as the second-best symbol survivor, PC_(n) ⁵ is selected as the second-best phase survivor, symbol candidate SC_(n) ²⁵ is selected as the third-best symbol survivor, and PC_(n) ²⁵ is selected as the third-best phase survivor.

Although the implementations described with reference to FIGS. 5A-6B use one phase survivor per symbol survivor. Other example implementations may use PSu (e.g., PSu<Su) phase survivors that are used commonly for each symbol survivor. In such an implementation, each of the phase survivors PS_(n−1) ¹ . . . PS_(n−1) ^(PSu) may be duplicated P times to generate phase successors, and then duplicated M*Su times to be associated with corresponding symbols successors. The number of symbol candidates in such an implementation would be M*Su*PSu*P.

FIG. 6C is a flowchart illustrating dynamic control of sequence estimation parameters based on characteristics of a received partial response signal. The process begins with block 620 when a partial response signal received by a receiver (e.g., a receiver comprising components 108, 109, 110, 112, and 114). Next, in block 622, characteristics (e.g., signal-to-noise ratio, symbol error rate, bit error rate, etc.) of the received signal are measured (e.g., in the front-end 108 and/or by a digital signal processing circuit downstream from the de-mapper 114). For example, the metrics calculated by the sequence estimation circuit 112 may be used in generating an estimate of SNR. In block 624, a value one or more parameters (e.g., Su, PSu, P, Q(see below), q1 (see below), q2 (see below), q3(see below)) may be configured based on the characteristics measured in block 622 and/or based on metrics calculated by the sequence estimation circuit 112.. Parameters may be configured during run-time (e.g., in, or near, real-time) based, for example, on recently received signals, signals currently being received, metrics calculated by the sequence estimation circuit 112 for recently-received signals, and/or metrics calculated by the sequence estimation circuit 112 for signals currently being received. Parameters maybe configured as needed to, for example, adjust delay for equalizer and/or carrier recovery loop bandwidth, to trade off complexity vs. accuracy of the sequence estimation process, etc. In block 626, sequence estimation may be performed on the received signal using the parameter values determined in block 624.

FIG. 7 is a diagram illustrating initialization of the sequence estimation process. In FIG. 7 it is again assumed, for illustration, that M=4 (a symbol alphabet of α, β, χ, δ), Su=3 (three symbol survivors are selected each iteration), Psu=Su (three phase survivors are selected each iteration), P=3 (a phase alphabet of plus, minus, and zero), and that Q (vector length) is 4. On the far left of FIG. 7 is shown symbol survivors 702 after receipt of a preamble sequence. Because the preamble is a deterministic sequence, all symbol survivors are forced to the same values. From the survivors 702 are generated the candidates 704 and metrics 706 are calculated based on the candidates 704. In the example implementation shown, since the survivors were all the same, there are only four unique symbol candidates. The metrics for the four candidates are, respectively, D1, D2, D3, and D4. Accordingly, if the three candidates corresponding to the best three metrics were chosen, then the three candidates corresponding to D1 would all be chosen and the survivors for the next iteration would again all be identical. Accordingly, the three best, non-redundant symbol candidates are selected (as indicated by the heavy lines). Consequently, one of the candidates having the metric value D1 is selected, one of the candidates having the metric value D2 is selected, and one of the candidates having metric value D3 is selected, such that three non-redundant survivors are used for the next iteration.

FIG. 8A depicts an example implementation of the phase buffer shown in FIG. 3. In the example implementation depicted, the depth of the phase buffer 312 is Q and the phase value stored at element q is represented as Z_(q), for q from 1 to Q. In the example implementation depicted, the value stored in element q3 is output as the signal 234. For each iteration of the sequence estimation process, Q elements of the phase buffer 312 storing Q values of PS_(n−1) ¹ may be overwritten with Q values of PS_(n) ¹.

FIGS. 8B depicts an example implementation of the symbol buffer shown in FIG. 3. In the example implementation depicted, the depth of the symbol buffer 310 is Q and the symbol value stored at element q is represented as X_(q), for q from 1 to Q. For each iteration of the sequence estimation process, Q elements of the symbol buffer 310 storing Q values of SS_(n−1) ¹ may be overwritten with Q values of SS_(n) ¹. In the example implementation depicted, the value(s) stored in one or more elements starting with index q1 (e.g., values stored in elements q1 through q1+L) is/are output as the signal 317 and the value(s) stored in one or more elements starting with index q2 (e.g., values stored in elements q2 through q2+L) is/are output as the signal 132. Because the value(s) output as the signal 317 start from a lower-indexed element of the symbol buffer, the delay between receiving a signal sample and outputting the corresponding value of signal 317 is shorter than the delay between receiving a signal sample and outputting the corresponding value of the signal 132. Because the value(s) output as the signal 132 start from a higher-indexed element, however, it/they is/are likely to be less error-prone. These concepts are further illustrated with reference to in FIGS. 8C and 8D. In an example implementation, q2 is equal to q3.

FIG. 8C depicts contents of an example symbol buffer over a plurality of iterations of a sequence estimation process. In the example implementation shown in FIG. 8C, the symbol buffer 310 comprises four elements with the signal 317 corresponding to the contents of the first element (for simplicity of illustration, in FIGS. 8C and 8D, it is assumed only one element is output as signal 317 on each iteration) and the signal 132 corresponding to the fourth element (for simplicity of illustration, in FIGS. 8C and 8D, it is assumed only one element is output as signal 132 on each iteration). In the example implementation depicted, during each iteration of the sequence estimation process, candidates are generated by duplicating the survivors from the previous iteration, shifting the values by one element, and the appending a new value into the vacated element. Accordingly, ideally each survivor would differ from the previous survivor only in the lowest-indexed element (corresponding to the most-recent symbol). Where other elements of the most-recent survivor differ from corresponding elements of the previous survivor, such difference indicates that there is an error in those elements (either in the most-recent survivor or in the previous survivor). Given the convolutional nature of the partial response signal, symbols at higher indexes in the buffer are more reliable. Thus the symbol values will tend to converge as they move toward the right in FIG. 8C.

Shown are the contents of example symbol buffer 310 at times n−3, n−2, n−1, and n. At time n−3, a symbol survivor having values α, β, χ, δ is stored in the symbol buffer 310. Accordingly, as shown in FIG. 8D, the value of signal 317 at time n−3 is ‘α’ and the value of signal 132 is ‘δ.’ At time n−2, a new symbol survivor having values δ, β, β, χ is stored in the symbol buffer 310. Accordingly, as shown in FIG. 8D, the value of signal 317 at time n−2 is ‘β’ and the value of signal 132 is ‘χ.’ At time n−1, a new symbol survivor having values χ, δ, β, β is stored in the symbol buffer 310. Accordingly, as shown in FIG. 8D, the value of signal 317 at time n−1 is ‘χ’ and the value of signal 132 is ‘β.’ At time n, a new symbol survivor having values β, χ, δ, β is stored in the symbol buffer 310. Accordingly, as shown in FIG. 8D, the value of signal 317 at time n is ‘β’ and the value of signal 132 is ‘β.’ Thus, in the example scenario depicted in FIG. 8C, the value in the first element of the symbol buffer 310 at time n−3 was erroneous and the symbol did not converge until it reached the second element (q=2) of the buffer 310. That is, at time n−2 the symbol changed from α to β and then remained β at times n−1 and n. This illustrates the consequence of taking signal 317 from the first element of the symbol buffer 310 and taking the signal 132 from the fourth element of the symbol buffer 312. Namely, the signal 317 has less delay than the signal 132 but is also more error prone than the signal 132.

In FIG. 8D, the values of the signals are shown for times n−3 to time n+3. The dashed lines illustrate the delay between the signal 317 and the signal 132.

FIG. 9 is a flowchart illustrating an example process for communications using partial response signaling. The process begins with block 902 in which symbols are processed via a partial response pulse shaping filter (e.g., filter 104) to generate a partial response signal. In block 904, the partial response signal is transmitted via a non-linear circuit (e.g., circuit 106). In block 906, the transmitted signal propagates through a channel and arrives at a receiver. In block 908, the signal is processed via a non-linear circuit (e.g., front-end 108) of the receiver. In block 910, the received signal is filtered (e.g., by input filter 109). In block 912, a timing carrier is removed from the received signal (e.g., by circuit 110). In block 914, an estimated value of a transmitted symbol is generated via a sequence estimation process. The sequence estimation process may be a reduced-complexity sequence estimation process. That is, the number of survivors may be much less than the Maximum Likelihood state space. In block 916, the estimated value of the transmitted symbol may be output to a de-mapper (e.g., de-mapper 114). In an example implementation, the size of the ML state space may be N̂(L−1) where N is the size of the symbol alphabet and LTx≦L≦LTx+LRx−1.

FIG. 10 is a flowchart illustrating an example process for equalization, phase error correction, and sequence estimation for reception of partial response signals. The process begins with block 1002 in which a received partial response signal is processed via a non-linear circuit (e.g., front-end 108), via an input filter (e.g., filter 109) and a timing pilot removal circuit (e.g., circuit 110). In block 1004, the signal resulting from the processing of block 1002 is equalized (e.g., via equalizer 202). In block 1006, the phase error of the equalized partial response signal is coarsely corrected (e.g., via phase adjust circuit 206). In block 1008, the coarsely phase corrected partial response signal is buffered (e.g., in buffer 212). In block 1010, the buffered samples are input to a sequence estimation process (e.g., performed by sequence estimation circuit 112). In block 1012, two symbol estimates resulting from the sequence estimation process (e.g., symbol estimates at indexes q1 and q2 of buffer 310), along with symbol estimates from previous iterations of the sequence estimation, are convolved with tap coefficients (e.g., tap coefficients corresponding to response ĥ) to generate partial response signals (e.g., signals 205 and 209). In block 1014, a non-linearity model (e.g., modeling the nonlinearity of the non-linear circuit via which the symbols were processed in block 1002) is applied to the first of the partial response signals generated in block 1012, resulting in a partial response feedback signal for controlling the coarse phase correction in block 1006 (e.g., to generate signal 207). In block 1016, a non-linearity model (e.g., modeling the nonlinearity of the non-linear circuit via which the symbols were processed in block 1002) is applied to the second of the partial response signals generated in block 1012, and the resulting signal (e.g., signal 203) is subtracted from the output of the equalizer to generate a partial response feedback signal (e.g., signal 201) for controlling the equalizer.

FIG. 11 is a flowchart illustrating an example process for sequence estimation for reception of partial response signals. The process begins with block 1102 in which a first partial response vector (e.g., PR1) is input to a vector calculation circuit (e.g., circuit 402). In block 1104, a plurality of second partial response vectors (e.g., PR2 _(n) ¹ . . . PR2 _(n) ^(M×Su×P)) are generated based on the first partial response vector and a plurality of phase candidate vectors (e.g., PC_(n) ¹ . . . PC_(n) ^(M×Su×P)). In block 1106, a plurality of candidate symbol vectors (e.g., SC_(n) ¹ . . . SC_(n) ^(M×Su×P)) are convolved with a plurality of tap coefficients (e.g., tap coefficients corresponding to response ĥ) to generate a plurality of third partial response vectors (e.g., SCPR_(n) ¹ . . . SCPR_(n) ^(M×Su×P)). In block 1108, metrics (e.g., Euclidean distances between the vectors) are generated based on the second partial response vectors and the third partial response vector are generated (e.g., by circuit 406). In block 1110, one or more of the candidate vectors is selected based on the metrics generated in block 1108. In block 1112, a symbol a best one of the survivor vectors (e.g., survivor vector corresponding to the lowest metric) is output to as an estimated transmitted symbol to a de-mapper.

In an example implementation of this disclosure, a receiver may receive a single-carrier inter-symbol correlated (ISC) signal (e.g., signal 118 or signal 120) that was generated by passage of symbols through a non-linear circuit (e.g., circuit 106 and/or circuit 108). The receiver may generate estimated values of the transmitted symbols using a sequence estimation process (e.g., implemented by circuit 112) and a model of non-linear circuit (e.g., implemented by circuit(s) 236 a and/or 236 b). The ISC signal may be a partial response signal generated via a partial response filter (e.g., a partial response filter comprising filter(s) 104 and/or 109). The receiver may generate an ISC feedback signal (e.g., signal 203), and control an equalizer of the receiver based on the ISC feedback signal. The receiver may generate an ISC feedback signal (e.g., signal 207), and control a carrier circuit based on the ISC feedback signal.

The sequence estimation process may comprise convolving each one of a plurality of candidate symbol vectors (e.g., each one of vectors SC_(n) ¹ . . . SC_(n) ^(M×Su×P)) with a plurality of tap coefficients to generate a corresponding one of a plurality of first ISC vectors (e.g., one of vectors SCPR_(n) ¹ . . . SCPR_(n) ^(M×Su×P)). The sequence estimation process may comprise generating a plurality of metrics (e.g., metrics D_(n) ¹ . . . D_(n) ^(M×Su×P)), each one of the metrics corresponding to the result of a cost function calculated using one of the first ISC vectors (e.g., SCPR_(n) ¹) and a second ISC vector (e.g., PR2 _(n) ¹). The plurality of tap coefficients may be based on tap coefficients of a partial response filter (e.g., tap coefficients of filter(s) 104 and/or 109). The sequence estimation process may comprise selecting one of the candidate symbol vectors (SC_(n) ¹) based on the plurality of metrics. The sequence estimation process may comprise outputting a symbol of the selected candidate symbol vector (e.g., the symbol at index q2 of symbol candidate SC_(n) ¹) as one of the estimated values of the transmitted symbols.

Other implementations may provide a non-transitory computer readable medium and/or storage medium, and/or a non-transitory machine readable medium and/or storage medium, having stored thereon, a machine code and/or a computer program having at least one code section executable by a machine and/or a computer, thereby causing the machine and/or computer to perform the processes as described herein.

Methods and systems disclosed herein may be realized in hardware, software, or a combination of hardware and software. Methods and systems disclosed herein may be realized in a centralized fashion in at least one computing system, or in a distributed fashion where different elements are spread across several interconnected computing systems. Any kind of computing system or other apparatus adapted for carrying out the methods described herein is suited. A typical combination of hardware and software may be a general-purpose computing system with a program or other code that, when being loaded and executed, controls the computing system such that it carries out methods described herein. Another typical implementation may comprise an application specific integrated circuit (ASIC) or chip with a program or other code that, when being loaded and executed, controls the ASIC such that is carries out methods described herein.

While methods and systems have been described herein with reference to certain implementations, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the scope of the present method and/or system. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the present disclosure without departing from its scope. Therefore, it is intended that the present method and/or system not be limited to the particular implementations disclosed, but that the present method and/or system will include all implementations falling within the scope of the appended claims. 

1-20. (canceled)
 21. A method comprising: in a receiver comprising a sequence estimation circuit: receiving a first sample of a first signal; generating, by said sequence estimation circuit, a first symbol vector based on said first sample of said signal; generating a partial response feedback signal based on said first symbol vector; receiving a second sample of said first signal; and processing said second sample of said first signal based on said partial response feedback signal.
 22. The method of claim 21, wherein: said first signal is non-linearly-distorted as a result of non-linearity of said signal path; said receiver comprises a non-linearity modeling circuit; and said method comprises determining, via said non-linearity modeling circuit, a model of said non-linearity of said signal path.
 23. The method of claim 22, wherein said model of said non-linearity of said signal path is represented as a polynomial or exponential expression.
 24. The method of claim 21, wherein symbols of said symbol vector are N-QAM symbols and N is an integer.
 25. The method of claim 21, wherein said generating said partial response feedback signal comprises performing the following: convolving said first symbol vector with a plurality of tap coefficients.
 26. The method of claim 25, wherein said generating said first symbol vector comprises performing the following: generating a plurality of metrics, each one of said plurality of metrics corresponding to one of a plurality of candidate symbol vectors; selecting one of said plurality of candidate symbol vectors based on said plurality of metrics; and outputting said selected one of said candidate symbol vectors as said symbol vector.
 27. The method of claim 26, wherein each of said plurality of metrics is based on a Euclidean distance.
 28. The method of claim 21, comprising: non-linearly distorting said partial response feedback signal; and processing said second sample of said first signal based on said non-linearly-distorted partial response feedback signal.
 29. The method of claim 28, comprising non-linearly-distorting said partial response feedback signal by applying said model of said non-linearity of said signal path to said partial response feedback signal.
 30. The method of claim 21, wherein said processing said second sample comprises one or both of: equalizing; and recovering a carrier frequency.
 31. A system comprising: a receiver comprising a sequence estimation circuit, wherein said receiver is operable to: receive a first sample of a first signal; generate, by said sequence estimation circuit, a first symbol vector based on said first sample of said signal; generate a partial response feedback signal based on said first symbol vector; receive a second sample of said first signal; and process said second sample of said first signal based on said partial response feedback signal.
 32. The system of claim 31, wherein: said first signal is non-linearly-distorted as a result of non-linearity of said signal path; said receiver comprises a non-linearity modeling circuit; and said receiver is operable to determine, via said non-linearity modeling circuit, a model of said non-linearity of said signal path.
 33. The system of claim 32, wherein said model of said non-linearity of said signal path is represented as a polynomial or exponential expression.
 34. The system of claim 31, wherein symbols of said symbol vector are N-QAM symbols and N is an integer.
 35. The system of claim 31, wherein said generation of said partial response feedback signal comprises convolution of said first symbol vector with a plurality of tap coefficients.
 36. The system of claim 35, wherein said generation of said first symbol vector comprises: generation of a plurality of metrics, each one of said plurality of metrics corresponding to one of a plurality of candidate symbol vectors; selection of one of said plurality of candidate symbol vectors based on said plurality of metrics; and output of said selected one of said candidate symbol vectors as said symbol vector.
 37. The system of claim 26, wherein each of said plurality of metrics is based on a Euclidean distance.
 38. The system of claim 21, wherein said receiver is operable to: non-linearly distort said partial response feedback signal; and process said second sample of said first signal based on said non-linearly-distorted partial response feedback signal.
 39. The system of claim 28, wherein said receiver is operable to non-linearly-distort said partial response feedback signal by application of said model of said non-linearity of said signal path to said partial response feedback signal.
 40. The system of claim 31, wherein said processing of said second sample comprises one or both of: equalization; and carrier frequency recovery. 